This invention concerns a method for forecasting the evolution of the magnitude of a data associated to a journey of an automotive vehicle.
Onboard electronic control devices are used on automotive vehicles, in particular on trucks, in order to control equipments or subsystems, such as an internal combustion engine or a gearbox. WO-A-2009/022194 discloses a system for adjusting the control parameters of an onboard electronic control device which allows the user to input some information with respect to a specific constraint to be followed during a given journey or type of journey. This is efficient insofar as the computations of the onboard electronic control device are accurate.
In the coming years, the cost of energy will increase, in particular for what concerns fossil energies like fuel. On the other hand, the impact of automotive vehicles on the environment must be decreased. A way to achieve a relatively small impact on the environment is to decrease fuel consumption and pollutant emissions of an automotive vehicle by choosing the best roads ahead of a vehicle and to anticipate, as much as possible, the power request and the engine work of the vehicle. For instance, an electronic horizon, including digitalized maps, can be used and give the different roads available for a journey. Such an electronic horizon can be combined with an automated manual transmission system which can advise the driver about the optimal gear to use at each point on a journey. All this is based on an accurate forecasting of the fuel consumption of the vehicle. If this forecasting is not accurate enough, the choice of the best road or the best gear to be used by the driver can be non-optimal. This can even lead to an increased fuel consumption.
The invention aims, according to an aspect thereof, at providing a method for forecasting the evolution of the magnitude of a data which allows to efficiently use computerized systems in order to select the best running conditions for a vehicle.
To this purpose, an aspect of the invention concerns a method for forecasting the evolution of the magnitude of a data associated to a journey of an automotive vehicle via a mathematical model, where said magnitude is expressed by a function of at least one input parameter, wherein this method includes at least the steps of:                a) defining a first model of the function used for computing the magnitude of the data, on the basis of the input parameter;        b) running the vehicle on a reference trip, the input parameter and the magnitude being measured at least at one time during or at the end of the reference trip;        c) computing a value of the magnitude of the data by using the first model of the function and the value of the parameter measured at step b);        d) comparing the values of the magnitude of the data at said time, respectively measured at step b) and computed at step c); and        e) depending on the result of the comparison of step d), adjusting the function in a way corresponding to the reduction of the difference between the measured value and the computed value.        
Thanks to aspects of the invention, one uses steps a) to e) as a self-learning process to help the onboard electronic computation system of the vehicle to improve the accuracy of the forecasting of the magnitude of the data which is associated to a given journey. For instance, the forecasting of the fuel consumption becomes more accurate, which enables the onboard computation means to efficiently select a road to be followed and/or a gear to be used during a given journey.
According to further aspects of the invention, such a method might incorporate one or several of the following features:                Steps b) to e) are implemented several times, on successive reference trips, the function adjusted in a step e) being used to compute the value of the magnitude of the data on the next step c).        The reference trip is a part of the journey to be followed by the vehicle, preferably an initial part thereof.        The computation of step c), the comparison of step d) and the adjustment of step e) occur in real time.        In step a), the first model is based on an initial data set for the function. Alternatively, this model is based on the last data set used for the function in a previous journey of the vehicle.        The magnitude of the data is expressed as a polynomial function of one parameter in the form:        
  Y  =            ∑              i        =        0            N        ⁢                  a        i            ⁢              x        i            where x is the input parameter, N is an integer larger than 1 and ai is a real number for i integer between 0 and N, and wherein the first model includes a set of N+1 real numbers corresponding to values of ai for i integer between 0 and N. In such a case, adjustment of the function advantageously occurs by adjusting the respective values of real numbers ai ford integer between 0 and N.                Alternatively, the magnitude of the data is expressed as the polynomial function of several parameters in the form:        
  Y  =      Ax    +          B      ⁢                        ⅆ          x                          ⅆ          t                      +    Cm    +    Dp    +    E  where x is a distance travelled during a reference trip, a journey or a part of a journey, m is the mass of the vehicle, p is the tire pressure, and A, B, C, D and E are real numbers, whereas the first model includes a set of real numbers corresponding to A, B, C, D and E. In such a case, adjustment of the function advantageously occurs by adjusting the respective values of numbers A, B, C, D and E.                According to another approach, the magnitude of the data is expressed as a function of a parameter which depends on the driver's behaviour and the first model includes a set of numbers which are advantageously selected in step e) depending on the driver's behaviour determined on the reference trip.        